Accuracy compensation method for discharge caustic alkali concentration measuring device in evaporation process

Patent metadata

Field	Value
Publication number	US-11893472-B2
Application number	US-202017309942-A
Country	US
Kind code	B2
Filing date	Jul 21, 2020
Priority date	Nov 11, 2019
Publication date	Feb 6, 2024
Grant date	Feb 6, 2024

Abstract

Disclosed is an accuracy compensation method for discharge caustic alkali concentration measuring device in evaporation process, comprising following steps: step 1. collecting process data of instrument values and laboratory values of alkali liquor refractive index, temperature and caustic alkali concentration in the evaporation process; step 2. performing sliding average filtering, time series matching and normalization on the process data collected in step 1 to obtain preprocessed process data; step 3. inputting the preprocessed process data into an accuracy compensation model of the caustic alkali concentration measuring device to obtain compensation values; step 4. adding the compensation values of the caustic alkali concentration to the instrument values to realize on-line compensation of the caustic alkali concentration. The disclosed can accurately compensate the concentration value measured by the on-line instrument, and the compensated concentration value can follow the actual change trend; moreover, the measurement accuracy can meet the needs of actual production.

First claim

The invention claimed is:

1. A method for compensating an accuracy of a discharge caustic alkali concentration measuring device, wherein the device is used monitoring an alkali liquor in an evaporation process, comprising the following steps:
step 1. measuring the alkali liquor during the evaporation process to obtain process data comprising a refractive index, a temperature, an instrument value of alkalinity measured real-time using an on-line instrument, and a laboratory value of alkalinity measured off-line in a laboratory;
step 2. performing sliding average filtering, time series matching and normalization on the process data collected in step 1 to obtain preprocessed process data;
step 3. constructing an accuracy compensation model and inputting the preprocessed process data into the accuracy compensation model of the caustic alkali concentration measuring device to obtain a compensation value; and
step 4 adding the compensation value of the caustic alkali concentration to the instrument value to realize the on-line compensation of the caustic alkali concentration,
wherein constructing the accuracy compensation model comprises training a double-layer Long Short-Term Memory (LSTM) network using historical refractive index data and historical temperature data as input training data and errors between historical instrument values and historical laboratory values as output training data.

2. The compensation method according to claim 1 , wherein:
during the sliding average filtering process in step 2, a window length of the sliding average filtering is set, wherein a number of points of the sliding average filtering is N, and the filtering formula is:

X
⁡
(
t
)

=

1
N

⁢

∑

i
=
0

N
-
1

X
′

(

t
-
i

)

(
1
)

wherein X(t) is a value at time t after filtering, X′(t) is a value of original data at time t, and N is the window length of the sliding average filtering.

3. The compensation method according to claim 1 , wherein, during the time series matching process in step 2, the process data of a 2 hours duration is divided into 3 parts of 40 minutes in duration each, and an average value of the process data in each part corresponds to laboratory data of the previous sampling;
wherein a formula for the time series matching is:

{

X
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(
k
)

=

1
40

⁢

∑

i
=
0

39

X
⁡
(
i
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X
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(

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1

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=

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40

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79

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Assignees

• Univ Northeastern

Inventors

• Chai Tianyou
• Jia Yao
• Wang Liangyong

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/044 Primary

  Recurrent networks, e.g. Hopfield networks · CPC title

• G01N33/00 Primary

  Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00 · CPC title

• G06F17/17

  Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method ({G06F17/18 takes precedence } ; interpolation for numerical control G05B19/18) · CPC title